R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1579 + ,0 + ,4.0 + ,45.7 + ,2146 + ,0 + ,5.9 + ,81.9 + ,2462 + ,0 + ,7.1 + ,56.8 + ,3695 + ,0 + ,10.5 + ,65.1 + ,4831 + ,0 + ,15.1 + ,86.2 + ,5134 + ,0 + ,16.8 + ,35.1 + ,6250 + ,0 + ,15.3 + ,133.8 + ,5760 + ,0 + ,18.4 + ,34.5 + ,6249 + ,0 + ,16.1 + ,69.9 + ,2917 + ,0 + ,11.3 + ,98.3 + ,1741 + ,0 + ,7.9 + ,86.7 + ,2359 + ,0 + ,5.6 + ,58.2 + ,1511 + ,1 + ,3.4 + ,83.6 + ,2059 + ,0 + ,4.8 + ,83.5 + ,2635 + ,0 + ,6.5 + ,112.3 + ,2867 + ,0 + ,8.5 + ,134.3 + ,4403 + ,0 + ,15.1 + ,30.0 + ,5720 + ,0 + ,15.7 + ,44.5 + ,4502 + ,0 + ,18.7 + ,120.1 + ,5749 + ,0 + ,19.2 + ,43.4 + ,5627 + ,0 + ,12.9 + ,199.4 + ,2846 + ,0 + ,14.4 + ,68.1 + ,1762 + ,0 + ,6.2 + ,99.8 + ,2429 + ,0 + ,3.3 + ,69.5 + ,1169 + ,0 + ,4.6 + ,71.3 + ,2154 + ,1 + ,7.2 + ,167.8 + ,2249 + ,0 + ,7.8 + ,66.3 + ,2687 + ,0 + ,9.9 + ,41.9 + ,4359 + ,0 + ,13.6 + ,57.2 + ,5382 + ,0 + ,17.1 + ,72.3 + ,4459 + ,0 + ,17.8 + ,96.5 + ,6398 + ,0 + ,18.6 + ,172.1 + ,4596 + ,0 + ,14.7 + ,25.8 + ,3024 + ,0 + ,10.5 + ,105.1 + ,1887 + ,0 + ,8.6 + ,92.2 + ,2070 + ,0 + ,4.4 + ,109.3 + ,1351 + ,0 + ,2.3 + ,101.7 + ,2218 + ,0 + ,2.8 + ,29.1 + ,2461 + ,1 + ,8.8 + ,34.6 + ,3028 + ,0 + ,10.7 + ,46.7 + ,4784 + ,0 + ,13.9 + ,82.0 + ,4975 + ,0 + ,19.3 + ,34.4 + ,4607 + ,0 + ,19.5 + ,72.7 + ,6249 + ,0 + ,20.4 + ,44.4 + ,4809 + ,0 + ,15.3 + ,31.0 + ,3157 + ,0 + ,7.9 + ,64.0 + ,1910 + ,0 + ,8.3 + ,65.4 + ,2228 + ,0 + ,4.5 + ,64.5 + ,1594 + ,0 + ,3.2 + ,153.8 + ,2467 + ,0 + ,5.0 + ,48.8 + ,2222 + ,0 + ,6.6 + ,25.0 + ,3607 + ,1 + ,11.1 + ,37.2 + ,4685 + ,0 + ,12.8 + ,40.8 + ,4962 + ,0 + ,16.3 + ,78.4 + ,5770 + ,0 + ,17.4 + ,112.4 + ,5480 + ,0 + ,18.9 + ,122.7 + ,5000 + ,0 + ,15.8 + ,82.9 + ,3228 + ,0 + ,11.7 + ,67.6 + ,1993 + ,0 + ,6.4 + ,78.4 + ,2288 + ,0 + ,2.9 + ,65.7 + ,1580 + ,0 + ,4.7 + ,44.9 + ,2111 + ,0 + ,2.4 + ,80.9 + ,2192 + ,0 + ,7.2 + ,38.8 + ,3601 + ,0 + ,10.7 + ,46.1 + ,4665 + ,1 + ,13.4 + ,60.0 + ,4876 + ,0 + ,18.5 + ,53.9 + ,5813 + ,0 + ,18.3 + ,123.5 + ,5589 + ,0 + ,16.8 + ,69.5 + ,5331 + ,0 + ,16.6 + ,74.2 + ,3075 + ,0 + ,14.1 + ,47.0 + ,2002 + ,0 + ,6.1 + ,60.9 + ,2306 + ,0 + ,3.5 + ,51.4 + ,1507 + ,0 + ,1.7 + ,18.7 + ,1992 + ,0 + ,2.3 + ,88.1 + ,2487 + ,0 + ,4.5 + ,65.3 + ,3490 + ,0 + ,9.3 + ,46.0 + ,4647 + ,0 + ,14.2 + ,115.6 + ,5594 + ,1 + ,17.3 + ,25.8 + ,5611 + ,0 + ,23.0 + ,48.1 + ,5788 + ,0 + ,16.3 + ,202.3 + ,6204 + ,0 + ,18.4 + ,9.2 + ,3013 + ,0 + ,14.2 + ,56.3 + ,1931 + ,0 + ,9.1 + ,71.6 + ,2549 + ,0 + ,5.9 + ,93.0 + ,1504 + ,0 + ,7.2 + ,82.3 + ,2090 + ,0 + ,6.8 + ,95.4 + ,2702 + ,0 + ,8.0 + ,61.9 + ,2939 + ,0 + ,14.3 + ,0.0 + ,4500 + ,0 + ,14.6 + ,103.4 + ,6208 + ,0 + ,17.5 + ,99.2 + ,6415 + ,1 + ,17.2 + ,96.7 + ,5657 + ,0 + ,17.2 + ,56.9 + ,5964 + ,0 + ,14.1 + ,57.6 + ,3163 + ,0 + ,10.5 + ,65.2 + ,1997 + ,0 + ,6.8 + ,71.7 + ,2422 + ,0 + ,4.1 + ,89.2 + ,1376 + ,0 + ,6.5 + ,70.7 + ,2202 + ,0 + ,6.1 + ,35.4 + ,2683 + ,0 + ,6.3 + ,140.5 + ,3303 + ,0 + ,9.3 + ,45.4 + ,5202 + ,0 + ,16.4 + ,53.9 + ,5231 + ,0 + ,16.1 + ,69.9 + ,4880 + ,0 + ,18.0 + ,101.9 + ,7998 + ,1 + ,17.6 + ,89.3 + ,4977 + ,0 + ,14.0 + ,70.7 + ,3531 + ,0 + ,10.5 + ,72.4 + ,2025 + ,0 + ,6.9 + ,67.6 + ,2205 + ,0 + ,2.8 + ,43.3 + ,1442 + ,0 + ,0.7 + ,62.9 + ,2238 + ,0 + ,3.6 + ,57.1 + ,2179 + ,0 + ,6.7 + ,68.2 + ,3218 + ,0 + ,12.5 + ,47.1 + ,5139 + ,0 + ,14.4 + ,43.1 + ,4990 + ,0 + ,16.5 + ,64.5 + ,4914 + ,0 + ,18.7 + ,73.1 + ,6084 + ,0 + ,19.4 + ,37.7 + ,5672 + ,1 + ,15.8 + ,29.1 + ,3548 + ,0 + ,11.3 + ,105.0 + ,1793 + ,0 + ,9.7 + ,98.0 + ,2086 + ,0 + ,2.9 + ,80.8) + ,dim=c(4 + ,120) + ,dimnames=list(c('Huwelijken' + ,'Specialedag' + ,'Temperatuur' + ,'Neerslag') + ,1:120)) > y <- array(NA,dim=c(4,120),dimnames=list(c('Huwelijken','Specialedag','Temperatuur','Neerslag'),1:120)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Huwelijken Specialedag Temperatuur Neerslag 1 1579 0 4.0 45.7 2 2146 0 5.9 81.9 3 2462 0 7.1 56.8 4 3695 0 10.5 65.1 5 4831 0 15.1 86.2 6 5134 0 16.8 35.1 7 6250 0 15.3 133.8 8 5760 0 18.4 34.5 9 6249 0 16.1 69.9 10 2917 0 11.3 98.3 11 1741 0 7.9 86.7 12 2359 0 5.6 58.2 13 1511 1 3.4 83.6 14 2059 0 4.8 83.5 15 2635 0 6.5 112.3 16 2867 0 8.5 134.3 17 4403 0 15.1 30.0 18 5720 0 15.7 44.5 19 4502 0 18.7 120.1 20 5749 0 19.2 43.4 21 5627 0 12.9 199.4 22 2846 0 14.4 68.1 23 1762 0 6.2 99.8 24 2429 0 3.3 69.5 25 1169 0 4.6 71.3 26 2154 1 7.2 167.8 27 2249 0 7.8 66.3 28 2687 0 9.9 41.9 29 4359 0 13.6 57.2 30 5382 0 17.1 72.3 31 4459 0 17.8 96.5 32 6398 0 18.6 172.1 33 4596 0 14.7 25.8 34 3024 0 10.5 105.1 35 1887 0 8.6 92.2 36 2070 0 4.4 109.3 37 1351 0 2.3 101.7 38 2218 0 2.8 29.1 39 2461 1 8.8 34.6 40 3028 0 10.7 46.7 41 4784 0 13.9 82.0 42 4975 0 19.3 34.4 43 4607 0 19.5 72.7 44 6249 0 20.4 44.4 45 4809 0 15.3 31.0 46 3157 0 7.9 64.0 47 1910 0 8.3 65.4 48 2228 0 4.5 64.5 49 1594 0 3.2 153.8 50 2467 0 5.0 48.8 51 2222 0 6.6 25.0 52 3607 1 11.1 37.2 53 4685 0 12.8 40.8 54 4962 0 16.3 78.4 55 5770 0 17.4 112.4 56 5480 0 18.9 122.7 57 5000 0 15.8 82.9 58 3228 0 11.7 67.6 59 1993 0 6.4 78.4 60 2288 0 2.9 65.7 61 1580 0 4.7 44.9 62 2111 0 2.4 80.9 63 2192 0 7.2 38.8 64 3601 0 10.7 46.1 65 4665 1 13.4 60.0 66 4876 0 18.5 53.9 67 5813 0 18.3 123.5 68 5589 0 16.8 69.5 69 5331 0 16.6 74.2 70 3075 0 14.1 47.0 71 2002 0 6.1 60.9 72 2306 0 3.5 51.4 73 1507 0 1.7 18.7 74 1992 0 2.3 88.1 75 2487 0 4.5 65.3 76 3490 0 9.3 46.0 77 4647 0 14.2 115.6 78 5594 1 17.3 25.8 79 5611 0 23.0 48.1 80 5788 0 16.3 202.3 81 6204 0 18.4 9.2 82 3013 0 14.2 56.3 83 1931 0 9.1 71.6 84 2549 0 5.9 93.0 85 1504 0 7.2 82.3 86 2090 0 6.8 95.4 87 2702 0 8.0 61.9 88 2939 0 14.3 0.0 89 4500 0 14.6 103.4 90 6208 0 17.5 99.2 91 6415 1 17.2 96.7 92 5657 0 17.2 56.9 93 5964 0 14.1 57.6 94 3163 0 10.5 65.2 95 1997 0 6.8 71.7 96 2422 0 4.1 89.2 97 1376 0 6.5 70.7 98 2202 0 6.1 35.4 99 2683 0 6.3 140.5 100 3303 0 9.3 45.4 101 5202 0 16.4 53.9 102 5231 0 16.1 69.9 103 4880 0 18.0 101.9 104 7998 1 17.6 89.3 105 4977 0 14.0 70.7 106 3531 0 10.5 72.4 107 2025 0 6.9 67.6 108 2205 0 2.8 43.3 109 1442 0 0.7 62.9 110 2238 0 3.6 57.1 111 2179 0 6.7 68.2 112 3218 0 12.5 47.1 113 5139 0 14.4 43.1 114 4990 0 16.5 64.5 115 4914 0 18.7 73.1 116 6084 0 19.4 37.7 117 5672 1 15.8 29.1 118 3548 0 11.3 105.0 119 1793 0 9.7 98.0 120 2086 0 2.9 80.8 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Specialedag Temperatuur Neerslag 505.099 525.423 259.288 2.906 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1590.77 -473.34 99.66 462.25 2144.46 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 505.099 196.234 2.574 0.0113 * Specialedag 525.423 245.616 2.139 0.0345 * Temperatuur 259.288 11.644 22.269 <2e-16 *** Neerslag 2.906 1.825 1.593 0.1139 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 706.8 on 116 degrees of freedom Multiple R-squared: 0.8152, Adjusted R-squared: 0.8104 F-statistic: 170.5 on 3 and 116 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.14822243 0.2964449 0.8517776 [2,] 0.06765399 0.1353080 0.9323460 [3,] 0.16525154 0.3305031 0.8347485 [4,] 0.50759684 0.9848063 0.4924032 [5,] 0.62785022 0.7442996 0.3721498 [6,] 0.58389969 0.8322006 0.4161003 [7,] 0.48852801 0.9770560 0.5114720 [8,] 0.40692746 0.8138549 0.5930725 [9,] 0.31854990 0.6370998 0.6814501 [10,] 0.26314084 0.5262817 0.7368592 [11,] 0.21753099 0.4350620 0.7824690 [12,] 0.22141393 0.4428279 0.7785861 [13,] 0.55407965 0.8918407 0.4459203 [14,] 0.48636804 0.9727361 0.5136320 [15,] 0.58413836 0.8317233 0.4158616 [16,] 0.84224327 0.3155135 0.1577567 [17,] 0.82478696 0.3504261 0.1752130 [18,] 0.85646825 0.2870635 0.1435317 [19,] 0.84794364 0.3041127 0.1520564 [20,] 0.86405138 0.2718972 0.1359486 [21,] 0.83922786 0.3215443 0.1607721 [22,] 0.81327634 0.3734473 0.1867237 [23,] 0.76957086 0.4608583 0.2304291 [24,] 0.72186508 0.5562698 0.2781349 [25,] 0.77465713 0.4506857 0.2253429 [26,] 0.74255927 0.5148815 0.2574407 [27,] 0.69738235 0.6052353 0.3026176 [28,] 0.67201024 0.6559795 0.3279898 [29,] 0.73776476 0.5244705 0.2622352 [30,] 0.69297283 0.6140543 0.3070272 [31,] 0.64303213 0.7139357 0.3569679 [32,] 0.69847285 0.6030543 0.3015271 [33,] 0.71183200 0.5763360 0.2881680 [34,] 0.67460003 0.6507999 0.3254000 [35,] 0.64243350 0.7151330 0.3575665 [36,] 0.62882679 0.7423464 0.3711732 [37,] 0.70848105 0.5830379 0.2915190 [38,] 0.67639330 0.6472134 0.3236067 [39,] 0.63574565 0.7285087 0.3642543 [40,] 0.60320386 0.7935923 0.3967961 [41,] 0.64293547 0.7141291 0.3570645 [42,] 0.60611058 0.7877788 0.3938894 [43,] 0.56341525 0.8731695 0.4365847 [44,] 0.54056802 0.9188640 0.4594320 [45,] 0.48637318 0.9727464 0.5136268 [46,] 0.49338509 0.9867702 0.5066149 [47,] 0.50848253 0.9830349 0.4915175 [48,] 0.45467810 0.9093562 0.5453219 [49,] 0.42353638 0.8470728 0.5764636 [50,] 0.37893378 0.7578676 0.6210662 [51,] 0.33229937 0.6645987 0.6677006 [52,] 0.30925648 0.6185130 0.6907435 [53,] 0.28151706 0.5630341 0.7184829 [54,] 0.29686408 0.5937282 0.7031359 [55,] 0.26060631 0.5212126 0.7393937 [56,] 0.25913442 0.5182688 0.7408656 [57,] 0.22529496 0.4505899 0.7747050 [58,] 0.19002291 0.3800458 0.8099771 [59,] 0.19675933 0.3935187 0.8032407 [60,] 0.18060405 0.3612081 0.8193960 [61,] 0.15079747 0.3015949 0.8492025 [62,] 0.14131032 0.2826206 0.8586897 [63,] 0.12031921 0.2406384 0.8796808 [64,] 0.17974200 0.3594840 0.8202580 [65,] 0.15244565 0.3048913 0.8475543 [66,] 0.15156273 0.3031255 0.8484373 [67,] 0.13404003 0.2680801 0.8659600 [68,] 0.12361329 0.2472266 0.8763867 [69,] 0.11590282 0.2318056 0.8840972 [70,] 0.10140402 0.2028080 0.8985960 [71,] 0.07952023 0.1590405 0.9204798 [72,] 0.08520801 0.1704160 0.9147920 [73,] 0.10537714 0.2107543 0.8946229 [74,] 0.09378190 0.1875638 0.9062181 [75,] 0.11165752 0.2233150 0.8883425 [76,] 0.19473269 0.3894654 0.8052673 [77,] 0.26809361 0.5361872 0.7319064 [78,] 0.22705125 0.4541025 0.7729487 [79,] 0.30370820 0.6074164 0.6962918 [80,] 0.27918558 0.5583712 0.7208144 [81,] 0.23182526 0.4636505 0.7681747 [82,] 0.39370435 0.7874087 0.6062957 [83,] 0.33574175 0.6714835 0.6642583 [84,] 0.38555894 0.7711179 0.6144411 [85,] 0.37468746 0.7493749 0.6253125 [86,] 0.34273295 0.6854659 0.6572671 [87,] 0.63114543 0.7377091 0.3688546 [88,] 0.57551109 0.8489778 0.4244889 [89,] 0.55076918 0.8984616 0.4492308 [90,] 0.52485296 0.9502941 0.4751470 [91,] 0.64024746 0.7195051 0.3597525 [92,] 0.58309069 0.8338186 0.4169093 [93,] 0.53246408 0.9350718 0.4675359 [94,] 0.45773626 0.9154725 0.5422637 [95,] 0.39248101 0.7849620 0.6075190 [96,] 0.34929055 0.6985811 0.6507094 [97,] 0.28661325 0.5732265 0.7133868 [98,] 0.77306847 0.4538631 0.2269315 [99,] 0.81934612 0.3613078 0.1806539 [100,] 0.75783277 0.4843345 0.2421672 [101,] 0.73823509 0.5235298 0.2617649 [102,] 0.65257860 0.6948428 0.3474214 [103,] 0.54769885 0.9046023 0.4523012 [104,] 0.44196004 0.8839201 0.5580400 [105,] 0.34792661 0.6958532 0.6520734 [106,] 0.57692889 0.8461422 0.4230711 [107,] 0.41506029 0.8301206 0.5849397 > postscript(file="/var/www/html/rcomp/tmp/1eild1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/26r2g1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/36r2g1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/46r2g1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5hiji1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 120 Frequency = 1 1 2 3 4 5 6 -96.072801 -126.930269 -49.127419 278.169159 160.118461 170.841800 7 8 9 10 11 12 1388.919184 383.724031 1366.203253 -803.751973 -1064.457643 232.736433 13 14 15 16 17 18 -644.073134 66.636923 118.144036 -232.372313 -104.545470 1014.739573 19 20 21 22 23 24 -1200.844853 139.426858 1197.555914 -1590.774925 -640.740223 866.258345 25 26 27 28 29 30 -736.048084 -1231.082832 -471.239545 -506.830764 161.334919 232.939566 31 32 33 34 35 36 -941.895696 569.954537 204.376532 -509.084271 -1115.944426 106.368854 37 38 39 40 41 42 -46.037184 902.318550 -951.820481 -387.211960 436.471249 -634.344968 43 44 45 46 47 48 -1165.515323 325.374345 246.690498 417.516179 -937.268084 368.643847 49 50 51 52 53 54 -187.816909 523.629077 -67.061702 -409.740460 742.429610 2.641703 55 56 57 58 59 60 426.608958 -282.259022 157.207432 -507.242858 -399.402334 840.017814 61 62 63 64 65 66 -274.249669 748.485751 -292.742224 187.531841 -14.368422 -582.587730 67 68 69 70 71 72 203.988998 525.863850 306.061768 -1222.664697 -261.754914 744.005326 73 74 75 76 77 78 506.761771 634.488982 625.318778 439.826349 124.031823 2.803184 79 80 81 82 83 84 -997.529148 468.546704 901.254325 -1337.622467 -1141.718150 243.809405 85 86 87 88 89 90 -1107.167828 -455.525434 -57.309364 -1273.924613 -91.226273 877.043742 91 92 93 94 95 96 643.672828 526.768288 1635.528144 -254.121475 -479.645277 594.572746 97 98 99 100 101 102 -1019.952397 12.356647 136.043064 254.570150 287.918080 348.203253 103 104 105 106 107 108 -588.447605 2144.464320 636.383994 92.952908 -465.658149 848.048582 109 110 111 112 113 114 572.590212 633.510364 -261.544254 -665.093760 774.883468 19.182073 115 116 117 118 119 120 -652.247073 439.135276 460.144998 -192.224423 -1512.018502 594.132144 > postscript(file="/var/www/html/rcomp/tmp/6hiji1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 120 Frequency = 1 lag(myerror, k = 1) myerror 0 -96.072801 NA 1 -126.930269 -96.072801 2 -49.127419 -126.930269 3 278.169159 -49.127419 4 160.118461 278.169159 5 170.841800 160.118461 6 1388.919184 170.841800 7 383.724031 1388.919184 8 1366.203253 383.724031 9 -803.751973 1366.203253 10 -1064.457643 -803.751973 11 232.736433 -1064.457643 12 -644.073134 232.736433 13 66.636923 -644.073134 14 118.144036 66.636923 15 -232.372313 118.144036 16 -104.545470 -232.372313 17 1014.739573 -104.545470 18 -1200.844853 1014.739573 19 139.426858 -1200.844853 20 1197.555914 139.426858 21 -1590.774925 1197.555914 22 -640.740223 -1590.774925 23 866.258345 -640.740223 24 -736.048084 866.258345 25 -1231.082832 -736.048084 26 -471.239545 -1231.082832 27 -506.830764 -471.239545 28 161.334919 -506.830764 29 232.939566 161.334919 30 -941.895696 232.939566 31 569.954537 -941.895696 32 204.376532 569.954537 33 -509.084271 204.376532 34 -1115.944426 -509.084271 35 106.368854 -1115.944426 36 -46.037184 106.368854 37 902.318550 -46.037184 38 -951.820481 902.318550 39 -387.211960 -951.820481 40 436.471249 -387.211960 41 -634.344968 436.471249 42 -1165.515323 -634.344968 43 325.374345 -1165.515323 44 246.690498 325.374345 45 417.516179 246.690498 46 -937.268084 417.516179 47 368.643847 -937.268084 48 -187.816909 368.643847 49 523.629077 -187.816909 50 -67.061702 523.629077 51 -409.740460 -67.061702 52 742.429610 -409.740460 53 2.641703 742.429610 54 426.608958 2.641703 55 -282.259022 426.608958 56 157.207432 -282.259022 57 -507.242858 157.207432 58 -399.402334 -507.242858 59 840.017814 -399.402334 60 -274.249669 840.017814 61 748.485751 -274.249669 62 -292.742224 748.485751 63 187.531841 -292.742224 64 -14.368422 187.531841 65 -582.587730 -14.368422 66 203.988998 -582.587730 67 525.863850 203.988998 68 306.061768 525.863850 69 -1222.664697 306.061768 70 -261.754914 -1222.664697 71 744.005326 -261.754914 72 506.761771 744.005326 73 634.488982 506.761771 74 625.318778 634.488982 75 439.826349 625.318778 76 124.031823 439.826349 77 2.803184 124.031823 78 -997.529148 2.803184 79 468.546704 -997.529148 80 901.254325 468.546704 81 -1337.622467 901.254325 82 -1141.718150 -1337.622467 83 243.809405 -1141.718150 84 -1107.167828 243.809405 85 -455.525434 -1107.167828 86 -57.309364 -455.525434 87 -1273.924613 -57.309364 88 -91.226273 -1273.924613 89 877.043742 -91.226273 90 643.672828 877.043742 91 526.768288 643.672828 92 1635.528144 526.768288 93 -254.121475 1635.528144 94 -479.645277 -254.121475 95 594.572746 -479.645277 96 -1019.952397 594.572746 97 12.356647 -1019.952397 98 136.043064 12.356647 99 254.570150 136.043064 100 287.918080 254.570150 101 348.203253 287.918080 102 -588.447605 348.203253 103 2144.464320 -588.447605 104 636.383994 2144.464320 105 92.952908 636.383994 106 -465.658149 92.952908 107 848.048582 -465.658149 108 572.590212 848.048582 109 633.510364 572.590212 110 -261.544254 633.510364 111 -665.093760 -261.544254 112 774.883468 -665.093760 113 19.182073 774.883468 114 -652.247073 19.182073 115 439.135276 -652.247073 116 460.144998 439.135276 117 -192.224423 460.144998 118 -1512.018502 -192.224423 119 594.132144 -1512.018502 120 NA 594.132144 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -126.930269 -96.072801 [2,] -49.127419 -126.930269 [3,] 278.169159 -49.127419 [4,] 160.118461 278.169159 [5,] 170.841800 160.118461 [6,] 1388.919184 170.841800 [7,] 383.724031 1388.919184 [8,] 1366.203253 383.724031 [9,] -803.751973 1366.203253 [10,] -1064.457643 -803.751973 [11,] 232.736433 -1064.457643 [12,] -644.073134 232.736433 [13,] 66.636923 -644.073134 [14,] 118.144036 66.636923 [15,] -232.372313 118.144036 [16,] -104.545470 -232.372313 [17,] 1014.739573 -104.545470 [18,] -1200.844853 1014.739573 [19,] 139.426858 -1200.844853 [20,] 1197.555914 139.426858 [21,] -1590.774925 1197.555914 [22,] -640.740223 -1590.774925 [23,] 866.258345 -640.740223 [24,] -736.048084 866.258345 [25,] -1231.082832 -736.048084 [26,] -471.239545 -1231.082832 [27,] -506.830764 -471.239545 [28,] 161.334919 -506.830764 [29,] 232.939566 161.334919 [30,] -941.895696 232.939566 [31,] 569.954537 -941.895696 [32,] 204.376532 569.954537 [33,] -509.084271 204.376532 [34,] -1115.944426 -509.084271 [35,] 106.368854 -1115.944426 [36,] -46.037184 106.368854 [37,] 902.318550 -46.037184 [38,] -951.820481 902.318550 [39,] -387.211960 -951.820481 [40,] 436.471249 -387.211960 [41,] -634.344968 436.471249 [42,] -1165.515323 -634.344968 [43,] 325.374345 -1165.515323 [44,] 246.690498 325.374345 [45,] 417.516179 246.690498 [46,] -937.268084 417.516179 [47,] 368.643847 -937.268084 [48,] -187.816909 368.643847 [49,] 523.629077 -187.816909 [50,] -67.061702 523.629077 [51,] -409.740460 -67.061702 [52,] 742.429610 -409.740460 [53,] 2.641703 742.429610 [54,] 426.608958 2.641703 [55,] -282.259022 426.608958 [56,] 157.207432 -282.259022 [57,] -507.242858 157.207432 [58,] -399.402334 -507.242858 [59,] 840.017814 -399.402334 [60,] -274.249669 840.017814 [61,] 748.485751 -274.249669 [62,] -292.742224 748.485751 [63,] 187.531841 -292.742224 [64,] -14.368422 187.531841 [65,] -582.587730 -14.368422 [66,] 203.988998 -582.587730 [67,] 525.863850 203.988998 [68,] 306.061768 525.863850 [69,] -1222.664697 306.061768 [70,] -261.754914 -1222.664697 [71,] 744.005326 -261.754914 [72,] 506.761771 744.005326 [73,] 634.488982 506.761771 [74,] 625.318778 634.488982 [75,] 439.826349 625.318778 [76,] 124.031823 439.826349 [77,] 2.803184 124.031823 [78,] -997.529148 2.803184 [79,] 468.546704 -997.529148 [80,] 901.254325 468.546704 [81,] -1337.622467 901.254325 [82,] -1141.718150 -1337.622467 [83,] 243.809405 -1141.718150 [84,] -1107.167828 243.809405 [85,] -455.525434 -1107.167828 [86,] -57.309364 -455.525434 [87,] -1273.924613 -57.309364 [88,] -91.226273 -1273.924613 [89,] 877.043742 -91.226273 [90,] 643.672828 877.043742 [91,] 526.768288 643.672828 [92,] 1635.528144 526.768288 [93,] -254.121475 1635.528144 [94,] -479.645277 -254.121475 [95,] 594.572746 -479.645277 [96,] -1019.952397 594.572746 [97,] 12.356647 -1019.952397 [98,] 136.043064 12.356647 [99,] 254.570150 136.043064 [100,] 287.918080 254.570150 [101,] 348.203253 287.918080 [102,] -588.447605 348.203253 [103,] 2144.464320 -588.447605 [104,] 636.383994 2144.464320 [105,] 92.952908 636.383994 [106,] -465.658149 92.952908 [107,] 848.048582 -465.658149 [108,] 572.590212 848.048582 [109,] 633.510364 572.590212 [110,] -261.544254 633.510364 [111,] -665.093760 -261.544254 [112,] 774.883468 -665.093760 [113,] 19.182073 774.883468 [114,] -652.247073 19.182073 [115,] 439.135276 -652.247073 [116,] 460.144998 439.135276 [117,] -192.224423 460.144998 [118,] -1512.018502 -192.224423 [119,] 594.132144 -1512.018502 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -126.930269 -96.072801 2 -49.127419 -126.930269 3 278.169159 -49.127419 4 160.118461 278.169159 5 170.841800 160.118461 6 1388.919184 170.841800 7 383.724031 1388.919184 8 1366.203253 383.724031 9 -803.751973 1366.203253 10 -1064.457643 -803.751973 11 232.736433 -1064.457643 12 -644.073134 232.736433 13 66.636923 -644.073134 14 118.144036 66.636923 15 -232.372313 118.144036 16 -104.545470 -232.372313 17 1014.739573 -104.545470 18 -1200.844853 1014.739573 19 139.426858 -1200.844853 20 1197.555914 139.426858 21 -1590.774925 1197.555914 22 -640.740223 -1590.774925 23 866.258345 -640.740223 24 -736.048084 866.258345 25 -1231.082832 -736.048084 26 -471.239545 -1231.082832 27 -506.830764 -471.239545 28 161.334919 -506.830764 29 232.939566 161.334919 30 -941.895696 232.939566 31 569.954537 -941.895696 32 204.376532 569.954537 33 -509.084271 204.376532 34 -1115.944426 -509.084271 35 106.368854 -1115.944426 36 -46.037184 106.368854 37 902.318550 -46.037184 38 -951.820481 902.318550 39 -387.211960 -951.820481 40 436.471249 -387.211960 41 -634.344968 436.471249 42 -1165.515323 -634.344968 43 325.374345 -1165.515323 44 246.690498 325.374345 45 417.516179 246.690498 46 -937.268084 417.516179 47 368.643847 -937.268084 48 -187.816909 368.643847 49 523.629077 -187.816909 50 -67.061702 523.629077 51 -409.740460 -67.061702 52 742.429610 -409.740460 53 2.641703 742.429610 54 426.608958 2.641703 55 -282.259022 426.608958 56 157.207432 -282.259022 57 -507.242858 157.207432 58 -399.402334 -507.242858 59 840.017814 -399.402334 60 -274.249669 840.017814 61 748.485751 -274.249669 62 -292.742224 748.485751 63 187.531841 -292.742224 64 -14.368422 187.531841 65 -582.587730 -14.368422 66 203.988998 -582.587730 67 525.863850 203.988998 68 306.061768 525.863850 69 -1222.664697 306.061768 70 -261.754914 -1222.664697 71 744.005326 -261.754914 72 506.761771 744.005326 73 634.488982 506.761771 74 625.318778 634.488982 75 439.826349 625.318778 76 124.031823 439.826349 77 2.803184 124.031823 78 -997.529148 2.803184 79 468.546704 -997.529148 80 901.254325 468.546704 81 -1337.622467 901.254325 82 -1141.718150 -1337.622467 83 243.809405 -1141.718150 84 -1107.167828 243.809405 85 -455.525434 -1107.167828 86 -57.309364 -455.525434 87 -1273.924613 -57.309364 88 -91.226273 -1273.924613 89 877.043742 -91.226273 90 643.672828 877.043742 91 526.768288 643.672828 92 1635.528144 526.768288 93 -254.121475 1635.528144 94 -479.645277 -254.121475 95 594.572746 -479.645277 96 -1019.952397 594.572746 97 12.356647 -1019.952397 98 136.043064 12.356647 99 254.570150 136.043064 100 287.918080 254.570150 101 348.203253 287.918080 102 -588.447605 348.203253 103 2144.464320 -588.447605 104 636.383994 2144.464320 105 92.952908 636.383994 106 -465.658149 92.952908 107 848.048582 -465.658149 108 572.590212 848.048582 109 633.510364 572.590212 110 -261.544254 633.510364 111 -665.093760 -261.544254 112 774.883468 -665.093760 113 19.182073 774.883468 114 -652.247073 19.182073 115 439.135276 -652.247073 116 460.144998 439.135276 117 -192.224423 460.144998 118 -1512.018502 -192.224423 119 594.132144 -1512.018502 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7ss1l1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8ss1l1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9l1ip1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10l1ip1292285786.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11o1gu1292285786.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12rkf01292285786.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13nud91292285786.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/149ubf1292285786.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/15uva31292285786.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16gd891292285786.tab") + } > > try(system("convert tmp/1eild1292285786.ps tmp/1eild1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/26r2g1292285786.ps tmp/26r2g1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/36r2g1292285786.ps tmp/36r2g1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/46r2g1292285786.ps tmp/46r2g1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/5hiji1292285786.ps tmp/5hiji1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/6hiji1292285786.ps tmp/6hiji1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/7ss1l1292285786.ps tmp/7ss1l1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/8ss1l1292285786.ps tmp/8ss1l1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/9l1ip1292285786.ps tmp/9l1ip1292285786.png",intern=TRUE)) character(0) > try(system("convert tmp/10l1ip1292285786.ps tmp/10l1ip1292285786.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.383 1.720 7.671